It's really a great course that I've waited for so long! Thanks a lot for providing the well-organized and easy -understanding materials for those new starters of deep learning like me! Hope to see the last part of sequence models in the nearly future!

Very well structured course & with Andrew Ng as the teacher, you really can't go wrong.

The assignments are extensive and help you put the thoery in practice.

I highly recommend this course for serious learners who want to advance in their AI learning.

The course was great you'll get intuition and deep understanding of how convolutional network work.... The material was missing segmentation part and assignment can be moved to tf 2.0... Overall it was great course and the assignments were also great.

The instructor was great, also the assignments were really a helpful one. I would really appreciate the instructor making CNN such an easy and interesting topic. Even the content of the syllabus was also great and hence I enjoyed the course even more.

The Neural Style Transfer assignment could benefit from some better descriptions and coding direction, but overall I loved all the assignments and learned a lot. I would like to learn more about Face Recognition and other Image Detection applications.

This provide me with a much deeper understanding of CNN and the basic building blocks for building CNN and facial recognition. I really enjoyed the programing exercising and learning how to do leverage additional frameworks like TensorFlow and Keras.

Great course! I learned so many stuff. Andrew's lectures are very intuitive and helpful. Those Jupyter notebooks are definitely worth time exploring. One last thing is that I wish some limits of the current CNN model can be mentioned for big picture.

Another great course in the Deep Learning specialization. It's been a wonderful experience diving into computer vision and discovering some exciting new applications and concepts.

Many thanks to the course team and a special thank you to Dr. Andrew Ng

In my opinion, this course is the most important and complete of the specialization course. Really, Andrew explains all the concepts necessary to create your own CNN or improving exist CNN. I loved this course. I know will be very useful in my Ph.D.

I really learn a lot from this course. However, It would be great if the course introduce how to annotate images and read annotated images to data set in order to get start computer vision project from scratch for audiences who has zero experiences

Great course which gives me a basic understanding on the technologies behind object detection, face recognition. Also, the programming assignments are very useful and give me hands on experience on how to build a basic system using the technologies.

Sometimes I had to close browser 1-2 times to make Kernel working during the submission of programming assignments. Interrupting or restarting the kernel was not helping. I'm sure it is not related to the course content, but just a technical issue,

The understanding of Deep Networks for Computer Vision gave me boost to go ahead and use them. I got some awareness about Keras, but I am a bit confused that should I stick with Tensorflow or with Keras. I Loved to work with Tensorflow in Course 2.

Excellent course on CNN which gets you familiar with many popular models from 2012-2016 including all the basic CNN models LeNet, AlexNet, VGG, Inception, ResNet, object localization/detection and YOLO, Face recognition with DeepFace, etc. Thanks!

The best course among whole specialisation. One gets to learn a lot about image processing as well as a whole set of reading materials with every programming assignment. Go through the reading materials if you want in depth knowledge of some topic.

As always, Andrew Ng manages to make the relatively complex seem simple. The programming assignments are excellent for demonstrating some diverse applications of deep learning and the optional backprop for conv layers was particularly illuminating.

Great course that gives insight in CNNs. The coding in frameworks is sometimes confusing and there were some bugs in the face recognition lab, but these are minor compared to the value of the course. Many ideas presented are state of the art.

This is the best course I have ever had on Coursera. The assignments and the lectures are amazing. After completing this course, I got the confidence that I am not far away from becoming a data scientist now. Thankyou Andrew Ng sir and the team

This was a really exciting course, presented in a way that was clear and is easy to understand. It is great that it uses such widely used frameworks such as TensorFlow and Keras — which will make the learning quite applicable to the real world.

It was good, but a few broken parts in the assignment almost made me quit. Once fixed it was the normal extremely useful, introduction into very cutting edge stuff. Thanks for putting in the work for this guys, it really is an amazing resource

CNNs are one of the most valuable types of Neural Networks that are being used nowadays, and this course is a great introduction to both the logic and math behind the algorithms. For sure one of the many stepping stones to master this subject!

I would not consider this course as on par with the prior courses on this specialization. It seemed rushed and there wasn't as much complex thinking we had to do on the assignments. Hopefully, you make it better over time! 5 stars nonetheless!

A very good course, I know it is not a programming course but I think more programming assignments would let us understand better on what the network is actually doing, maybe some mini exercises in between lecture. But overall a great course!

I think this course help me much in gaining intuition about Convolutional Neural Network such as what is it, how it works. Additionally, the program assignment is really helpful and good. I can do with many other famous Convolutional Network.

A very nice introductory course in Convolution Networks and some of the main algorithms today. Its a really start to become a practitioner in Deep Learning and keep focus on architectures and experiments. I really enjoyed every lecture of it.